This notebook runs through building PCA for metabolic cage data.

Before starting, a conda environment is required.
See bottom of this notebook for the development environment for MouseTrapper.py.

Useful docs:

Plotting PCA (Principal Component Analysis)

dataset

Overview

These datasets are from RMR data collected for mice after introduction to the metabolic cages (days 0-6, see image).

overview

SPF

Start of Met Cage run	Metabolic Cage #	Ear Tag	DOB	Gender	Genotype	Body Mass on BMR day (g)
3/10/2020	1	1555	11/18/19	M	WT	26.4
3/10/2020	2	1692	12/06/19	M	WT	25.6
3/10/2020	3	1693	12/06/19	M	WT	24.7
3/10/2020	4	1695	12/06/19	M	WT	25.6
3/10/2020	5	1699	12/06/19	M	WT	28.7
3/10/2020	6	1556	11/18/19	M	L-Bmal1-KO	26.2
3/10/2020	9	1557	11/18/19	M	L-Bmal1-KO	27.1
3/10/2020	10	1558	11/18/19	M	L-Bmal1-KO	26.9
3/10/2020	11	1559	11/18/19	M	L-Bmal1-KO	24.7
3/10/2020	12	1691	12/06/19	M	L-Bmal1-KO	28
3/10/2020	13	1694	12/06/19	M	L-Bmal1-KO	26.4
3/10/2020	14	1700	12/06/19	M	L-Bmal1-KO	27.7

GF

Cage	Ear Tag	Genotype	GF condition, day 6	GF condition, day 10
1	5650	L-Bmal1-KO	GF	contaminated
2	976	WT	GF	GF
3	977	WT	GF	contaminated
4	990	L-Bmal1-KO	likely contaminated	likely contaminated
5	979	L-Bmal1-KO	GF	contaminated
6	978	L-Bmal1-KO	GF	contaminated

For the first half of the experiment, only cage 4 had contamination. For the second leg of the experiment, cages 1,3,5 and 6 were contaminated, and it is likely cage 4 was also contaminated.

See the 16S rDNA gel below.

libraries

reqpkg <- c("ggplot2","ggfortify","ggpubr","dplyr","magrittr","reshape2","tibble","rgl","lfda","DT")
for (i in reqpkg) {
    print(i)
    print(packageVersion(i))
    suppressWarnings(suppressMessages(library(i, quietly=T, verbose=T, warn.conflicts=F,character.only=T)))
}

## [1] "ggplot2"
## [1] '3.3.0'
## [1] "ggfortify"
## [1] '0.4.10'
## [1] "ggpubr"
## [1] '0.2.5'
## [1] "dplyr"
## [1] '0.8.5'
## [1] "magrittr"
## [1] '1.5'
## [1] "reshape2"
## [1] '1.4.3'
## [1] "tibble"
## [1] '2.1.3'
## [1] "rgl"
## [1] '0.100.54'
## [1] "lfda"
## [1] '1.1.3'
## [1] "DT"
## [1] '0.13'

theme_set(theme_pubr())
knitr::knit_hooks$set(webgl = hook_webgl)

create data

This is a shell script that calls a python script to interpret the macro13 output to usable data by R.

shell("conda activate met_cages_development && cd py && python SPF_vs_GF.py")

The script pulls the last 2 days of data for both experiments, and procures a separate dataset for the day time and night time values.

Bodymass information is also exported but is unused in this notebook. The data is pointed to /data/R/ to dump files for R.

SPF_vs_GF.py

#!/usr/bin/env python

"""
A CLI python script that uses the mousetrapper library to prepare SPF vs GF mice data for import to R.
Must be run from same directory.
"""

import argparse
import numpy as np
import pandas as pd

import lib.mousetrapper as mt

__author__ = "Sumeed Yoyo Manzoor"
__email__ = "smanzoor@uchicago.edu"
__version__ = "0.0.1"
__experiment__ = "6 GF mice (10/12/2019) vs 12 SPF mice (3/10/2020)"

datasets = "../datasets/"
data = "../data/"
outfolder = data + "R/"

experiment_GF_RMR = mt.Experiment.experiment(
    info="""6 GF mice run on Oct. 2019""",
    RT_data=data + "GFvsSPF/" + "GF_RMR_201912101635_rt.csv",
    macro13_data=data + "GFvsSPF/" + "gf_rmr_201912101635_macro_One-Click Macro V2.32.2-slice3-VOC.mac_1.csv", 
    t_ctrl={"GF":[1,2,3,4,5,6]},
    t_exp={"SPF":[]},
    groups={
        "GF":[1,2,3,4,5,6],
        "SPF":[]
    },
    meta=["GF","GF","GF","GF","GF","GF"],
    covar=[31.2,28.2,27.4,27.8,27.5,27.1],
    verbose=True
    )

experiment_SPF_RMR = mt.Experiment.experiment(
    info="""Second run with revised protocol, WT vs L-Bmal1-KO""",
    RT_data=datasets + "Bmal_SPF_KF_RMR_2020-03-10_14_23_rt.csv",
    macro13_data=datasets + "bmal_spf_kf_rmr_2020-03-10_14_23_macro_One-Click Macro V2.32.2-slice3-VOC.mac_1.csv",
    t_ctrl={"SPF WT":np.arange(1,6).tolist()},
    t_exp={"SPF L-Bmal1-KO":[6] + np.arange(9,15).tolist()},
    groups={
        "SPF WT":np.arange(1,6).tolist(),
        "SPF L-Bmal1-KO":[6] + np.arange(9,15).tolist()
    },
    meta=np.array(["WT","L-Bmal1-KO"]).repeat([5,7]).tolist(),
    covar=[26.4,25.6,24.7,25.6,28.7,26.2,27.1,26.9,24.7,28,26.4,27.7],
    verbose=True
    )

experiment_GF_RMR.macro13_lightcycles(days=2)
experiment_SPF_RMR.macro13_lightcycles(days=2)

experiment_GF_RMR.macro13_dark.to_csv(outfolder + "GF_dark.csv", index=False)
experiment_GF_RMR.macro13_light.to_csv(outfolder + "GF_light.csv", index=False)
pd.DataFrame({"covar":experiment_GF_RMR.covar}).to_csv(outfolder + "GF_covar.csv", index=False)

experiment_SPF_RMR.macro13_dark.to_csv(outfolder + "SPF_dark.csv", index=False)
experiment_SPF_RMR.macro13_light.to_csv(outfolder + "SPF_light.csv", index=False)
pd.DataFrame({"covar":experiment_SPF_RMR.covar}).to_csv(outfolder + "SPF_covar.csv", index=False)

import data

SPF_dark <- read.csv2("data/R/SPF_dark.csv", sep = ",") %>% 
  set_colnames(paste0("SPF_",colnames(.)))
SPF_light <- read.csv2("data/R/SPF_light.csv", sep = ",") %>% 
  set_colnames(paste0("SPF_",colnames(.)))
SPF_covar <- read.csv2("data/R/SPF_covar.csv", sep = ",")

GF_dark <- read.csv2("data/R/GF_dark.csv", sep = ",")
GF_light <- read.csv2("data/R/GF_light.csv", sep = ",") %>% 
  set_colnames(paste0("GF_",colnames(.)))
GF_covar <- read.csv2("data/R/GF_covar.csv", sep = ",") %>% 
  set_colnames(paste0("GF_",colnames(.)))

functions

pca_mutate <- function(data, channel) {
  df <- data %>% select(contains(channel))
  SPF <- c(df[,8], df[,9], df[,10], df[,11], df[,12])
  GF <- c(df[,13], df[,14], df[,15], df[,16], df[,17])
  df <- list(SPF=SPF, GF=GF) %>% plyr::ldply(rbind) %>% t() %>% as.data.frame() %>% set_colnames(c("SPF","GF")) %>% slice(-1) %>% 
    mutate_all(function(x) as.numeric(as.character(x))) %>% 
    melt() %>% na.omit()
  
  return(df)
}

plot_pca <- function(pca, data, title="") {
  autoplot(pca, data = na.omit(data), colour = "id", loadings = TRUE, loadings.colour = "black", loadings.label = TRUE, loadings.label.colour = "black", loadings.label.size = 5) + ggtitle(title)
}

graph_groups <- function(data, channel, title="") {
  df <- data %>% select(contains(channel))
  df.observation <- 1:nrow(df)
  SPF <- c(df[,8], df[,9], df[,10], df[,11], df[,12])
  GF <- c(df[,13], df[,14], df[,15], df[,16], df[,17])
  df <- list(SPF=SPF, GF=GF) %>% plyr::ldply(rbind) %>% t() %>% 
    as.data.frame() %>% set_colnames(c("SPF","GF")) %>% slice(-1) %>%
    mutate_all(function(x) as.numeric(as.character(x))) %>% melt()
  df$observation <- rep(df.observation, 10)
  ggplot(df, aes(x=observation, y=value, color=variable)) + 
    ggtitle(title) + 
    geom_smooth()
}

graph_mice <- function(data, channel, title="") {
  df <- data %>% dplyr::select(contains(channel))
  df.observation <- 1:nrow(df)
  SPF <- c(df[,8], df[,9], df[,10], df[,11], df[,12])
  GF <- c(df[,13], df[,14], df[,15], df[,16], df[,17])
  df <- list(SPF=SPF, GF=GF) %>% plyr::ldply(rbind) %>% t() %>% as.data.frame() %>% set_colnames(c("SPF","GF")) %>% slice(-1) %>% 
    mutate_all(function(x) as.numeric(as.character(x)))
  df <- melt(df)
  df$observation <- rep(df.observation, 10)
  df$mouse <- c(rep("SPF_1", length(df.observation)), rep("SPF_2", length(df.observation)), rep("SPF_3", length(df.observation)), rep("SPF_4", length(df.observation)), rep("SPF_5", length(df.observation)), rep("WT_1", length(df.observation)), rep("WT_2", length(df.observation)), rep("WT_3", length(df.observation)), rep("WT_4", length(df.observation)), rep("WT_5", length(df.observation)))
  ggplot(df, aes(x=observation, y=value, color=mouse)) + 
    geom_line()
}

data prep

light <- SPF_light %>% 
  bind_cols(GF_light) %>% 
  dplyr::select(-contains(c("Enviro", "Time"))) %>% 
  mutate_all(function(x) as.numeric(as.character(x)))

dark <- SPF_dark %>% 
  bind_cols(GF_dark) %>% 
  dplyr::select(-contains(c("Enviro", "Time"))) %>% 
  mutate_all(function(x) as.numeric(as.character(x)))

datatables

Light

Hide

Select a tab above to view

VO2

light %>% select(contains("VO2")) %>% datatable(filter="bottom")

RQ

light %>% select(contains("RQ")) %>% datatable(filter="bottom")

BodyMass

light %>% select(contains("BodyMass")) %>% datatable(filter="bottom")

Food

light %>% select(contains("Food")) %>% datatable(filter="bottom")

Water

light %>% select(contains("Water")) %>% datatable(filter="bottom")

PedMeters

light %>% select(contains("PedMeters")) %>% datatable(filter="bottom")

Dark

Hide

Select a tab above to view

VO2

dark %>% select(contains("VO2")) %>% datatable(filter="bottom")

RQ

dark %>% select(contains("RQ")) %>% datatable(filter="bottom")

BodyMass

dark %>% select(contains("BodyMass")) %>% datatable(filter="bottom")

Food

dark %>% select(contains("Food")) %>% datatable(filter="bottom")

Water

dark %>% select(contains("Water")) %>% datatable(filter="bottom")

PedMeters

dark %>% select(contains("PedMeters")) %>% datatable(filter="bottom")

basic graphs

Some graphs to look at the data.

Light

Hide

Select a tab above to view

VO2

graph_groups(light, "VO2", title="Light VO2 by group")

## No id variables; using all as measure variables

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

graph_mice(light, "VO2", title="Light VO2 by mouse")

## No id variables; using all as measure variables

RQ

graph_groups(light, "RQ", title="Light RQ by group")

## No id variables; using all as measure variables

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

graph_mice(light, "RQ", title="Light RQ by mouse")

## No id variables; using all as measure variables

BodyMass

graph_groups(light, "BodyMass", title="Light BodyMass by group")

## No id variables; using all as measure variables

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

graph_mice(light, "BodyMass", title="Light BodyMass by mouse")

## No id variables; using all as measure variables

Food

graph_groups(light, "Food", title="Light Food by group")

## No id variables; using all as measure variables

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

graph_mice(light, "Food", title="Light Food by mouse")

## No id variables; using all as measure variables

Water

graph_groups(light, "Water", title="Light Water by group")

## No id variables; using all as measure variables

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

graph_mice(light, "Water", title="Light Water by mouse")

## No id variables; using all as measure variables

PedMeters

graph_groups(light, "PedMeters_R", title="Light PedMeters by group")

## No id variables; using all as measure variables

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

graph_mice(light, "PedMeters_R", title="Light PedMeters by mouse")

## No id variables; using all as measure variables

Dark

Hide

Select a tab above to view

VO2

graph_groups(dark, "VO2", title="Dark VO2 by group")

## No id variables; using all as measure variables

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

graph_mice(dark, "VO2", title="Dark VO2 by mouse")

## No id variables; using all as measure variables

RQ

graph_groups(dark, "RQ", title="Dark RQ by group")

## No id variables; using all as measure variables

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

graph_mice(dark, "RQ", title="Dark RQ by mouse")

## No id variables; using all as measure variables

BodyMass

graph_groups(dark, "BodyMass", title="Dark BodyMass by group")

## No id variables; using all as measure variables

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

graph_mice(dark, "BodyMass", title="Dark BodyMass by mouse")

## No id variables; using all as measure variables

Food

graph_groups(dark, "Food", title="Dark Food by group")

## No id variables; using all as measure variables

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

graph_mice(dark, "Food", title="Dark Food by mouse")

## No id variables; using all as measure variables

Water

graph_groups(dark, "Water", title="Dark Water by group")

## No id variables; using all as measure variables

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

graph_mice(dark, "Water", title="Dark Water by mouse")

## No id variables; using all as measure variables

PedMeters

graph_groups(dark, "PedMeters_R", title="Dark PedMeters by group")

## No id variables; using all as measure variables

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

graph_mice(dark, "PedMeters_R", title="Dark PedMeters by mouse")

## No id variables; using all as measure variables

PCA

PCA is run for light cycle and dark cycle values.

Important to note for most of the models I build below, I need the same number of mice in both groups (NAs are not allowed). Thus, I decided to use the five GF mice that remained GF, and the last 5 KO mice for SPF.

Light

VO2_light <- pca_mutate(light, "VO2")
RQ_light <- pca_mutate(light, "RQ")
Food_light <- pca_mutate(light, "Food")
Water_light <- pca_mutate(light, "Water")
BM_light <- pca_mutate(light, "BodyMass")
PedMeters_light <- pca_mutate(light, "PedMeters_R")

df_light <- RQ_light %>% set_colnames(c("id","RQ")) %>% add_column(VO2=VO2_light$value) %>% add_column(FoodIn=Food_light$value) %>% add_column(WaterIn=Water_light$value) %>% add_column(BodyMass=BM_light$value) %>% add_column(PedMeters=PedMeters_light$value)

PCA of all variables

df_light %>% select(-id) %>% prcomp(scale. = TRUE) %>% plot_pca(df_light, title = "PCA of all variables, light cycle")

Based on eigenvalues, some variables are driving differences more than others, or may be used alone to look deeper at how the data clusters.

Since body mass may be disproportionately driving differences in PCA (repeated measures for a generally low variance variable), PCA is run without it.

df_light %>% select(-id,-BodyMass) %>% prcomp(scale. = TRUE) %>% plot_pca(df_light, title="PCA of all variables besides BM, light cycle")

VO2 vs RQ

df_light %>% select(VO2,RQ) %>% prcomp(scale. = TRUE) %>% plot_pca(df_light, title="PCA VO2 and RQ, light cycle")

Food vs Water

df_light %>% select(FoodIn,WaterIn) %>% prcomp(scale. = TRUE) %>% plot_pca(df_light, title="PCA Food and Water, light cycle")

Dark

VO2_dark <- pca_mutate(dark, "VO2")
RQ_dark <- pca_mutate(dark, "RQ")
Food_dark <- pca_mutate(dark, "Food")
Water_dark <- pca_mutate(dark, "Water")
BM_dark <- pca_mutate(dark, "BodyMass")
PedMeters_dark <- pca_mutate(dark, "PedMeters_R")

df_dark <- RQ_dark %>% set_colnames(c("id","RQ")) %>% add_column(VO2=VO2_dark$value) %>% add_column(FoodIn=Food_dark$value) %>% add_column(WaterIn=Water_dark$value) %>% add_column(BodyMass=BM_dark$value) %>% add_column(PedMeters=PedMeters_dark$value)

PCA of all variables

df_dark %>% select(-id) %>% prcomp(scale. = TRUE) %>% plot_pca(df_dark, "PCA of all variables, dark cycle")

Based on eigenvalues, some variables are driving differences more than others, or may be used alone to look deeper at how the data clusters.

Since body mass may be disproportionately driving differences in PCA (repeated measures for a generally low variance variable), PCA is run without it.

df_dark %>% select(-id,-BodyMass) %>% prcomp(scale. = TRUE) %>% plot_pca(df_dark, title="PCA of all variables besides BM, dark cycle")

VO2 vs RQ

df_dark %>% select(VO2,RQ) %>% prcomp(scale. = TRUE) %>% plot_pca(df_dark, title="PCA VO2 and RQ, dark cycle")

Food vs Water

df_dark %>% select(FoodIn,WaterIn) %>% prcomp(scale. = TRUE) %>% plot_pca(df_dark, title="PCA Food and Water, dark cycle")

Local Fisher Discriminant Analysis (LDFA)

I attempted to model and plot ldfa. LDFA should give similar results to PCA, with the advantage of being sensitive to multimodality of data, i.e. multiple effects driving an observation, as well as sensitive to distinctiveness in the data that might be lost in reducing dimensions in PCA. In other words, a learning model like LDFA might account for more relationships than a principal component.

More information:

Tang Y, Cohen J, Local Fisher Discriminant Analysis on Beer Style Clustering.
Yang Y, Li W, lfda: An R Package for Local Fisher Discriminant Analysis and Visualization. The R Journal Vol. XX/YY, AAAA 20ZZ

Light

model <- lfda(df_light[-1], df_light[, 1], r=3, metric = "plain")
autoplot(model, data=df_light, frame = T, colour = "id")

plot(model, labels = df_light[, 1])

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Dark

model <- lfda(df_dark[-1], df_dark[, 1], r=3, metric = "plain")
autoplot(model, data=df_dark, frame = T, colour = "id")

plot(model, labels = df_dark[, 1])

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I also attempted Semi-supervised Local Fisher Discriminant Analysis (SELF). It would not finish the model on my laptop, but might compute on a cluster.

model <- self(df[-1], df[, 1], beta = 0.1, r=2, metric = "plain")
autoplot(model, data=df, frame = T, colour = "id")

session info

## R version 3.6.3 (2020-02-29)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 18362)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=English_United States.1252 
## [2] LC_CTYPE=English_United States.1252   
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C                          
## [5] LC_TIME=English_United States.1252    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] DT_0.13          lfda_1.1.3       rgl_0.100.54     tibble_2.1.3    
##  [5] reshape2_1.4.3   dplyr_0.8.5      ggpubr_0.2.5     magrittr_1.5    
##  [9] ggfortify_0.4.10 ggplot2_3.3.0   
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.0.0        xfun_0.12               purrr_0.3.3            
##  [4] splines_3.6.3           lattice_0.20-40         colorspace_1.4-1       
##  [7] vctrs_0.2.4             miniUI_0.1.1.1          htmltools_0.4.0        
## [10] mgcv_1.8-31             yaml_2.2.1              rlang_0.4.5            
## [13] manipulateWidget_0.10.1 pillar_1.4.3            later_1.0.0            
## [16] glue_1.3.2              withr_2.1.2             lifecycle_0.2.0        
## [19] plyr_1.8.6              stringr_1.4.0           munsell_0.5.0          
## [22] ggsignif_0.6.0          gtable_0.3.0            htmlwidgets_1.5.1      
## [25] evaluate_0.14           labeling_0.3            knitr_1.28             
## [28] fastmap_1.0.1           httpuv_1.5.2            crosstalk_1.1.0.1      
## [31] markdown_1.1            rARPACK_0.11-0          Rcpp_1.0.4.6           
## [34] xtable_1.8-4            scales_1.1.0            promises_1.1.0         
## [37] webshot_0.5.2           jsonlite_1.6.1          farver_2.0.3           
## [40] RSpectra_0.16-0         mime_0.9                gridExtra_2.3          
## [43] digest_0.6.25           stringi_1.4.6           shiny_1.4.0.2          
## [46] grid_3.6.3              tools_3.6.3             klippy_0.0.0.9500      
## [49] crayon_1.3.4            tidyr_1.0.2             pkgconfig_2.0.3        
## [52] Matrix_1.2-18           assertthat_0.2.1        rmarkdown_2.1          
## [55] R6_2.4.1                nlme_3.1-144            compiler_3.6.3

Python environment

environment.yml

name: Env5_metcages
channels:
  - anaconda
  - plotly
  - conda-forge
  - salilab
  - defaults
dependencies:
  - _r-mutex=1.0.0=anacondar_1
  - asn1crypto=1.3.0=py37_0
  - attrs=19.3.0=py_0
  - backcall=0.1.0=py37_0
  - blas=1.0=mkl
  - bleach=3.1.0=py37_0
  - ca-certificates=2020.1.1=0
  - certifi=2019.11.28=py37_1
  - cffi=1.14.0=py37h7a1dbc1_0
  - chardet=3.0.4=py37_1003
  - colorama=0.4.3=py_0
  - cryptography=2.8=py37h7a1dbc1_0
  - decorator=4.4.2=py_0
  - defusedxml=0.6.0=py_0
  - entrypoints=0.3=py37_0
  - icc_rt=2019.0.0=h0cc432a_1
  - icu=58.2=ha66f8fd_1
  - idna=2.8=py37_0
  - importlib_metadata=1.5.0=py37_0
  - intel-openmp=2019.4=245
  - ipykernel=5.1.4=py37h39e3cac_0
  - ipython=7.13.0=py37h5ca1d4c_0
  - ipython_genutils=0.2.0=py37_0
  - ipywidgets=7.5.1=py_0
  - jedi=0.16.0=py37_1
  - jinja2=2.11.1=py_0
  - jpeg=9b=hb83a4c4_2
  - jsonschema=3.2.0=py37_0
  - jupyter=1.0.0=py37_7
  - jupyter_client=6.1.2=py_0
  - jupyter_console=6.1.0=py_0
  - jupyter_contrib_core=0.3.3=py_2
  - jupyter_core=4.6.3=py37_0
  - jupyter_nbextensions_configurator=0.4.1=py37_0
  - libpng=1.6.37=h2a8f88b_0
  - libsodium=1.0.16=h9d3ae62_0
  - m2w64-bwidget=1.9.10=2
  - m2w64-bzip2=1.0.6=6
  - m2w64-expat=2.1.1=2
  - m2w64-fftw=3.3.4=6
  - m2w64-flac=1.3.1=3
  - m2w64-gcc-libgfortran=5.3.0=6
  - m2w64-gcc-libs=5.3.0=7
  - m2w64-gcc-libs-core=5.3.0=7
  - m2w64-gettext=0.19.7=2
  - m2w64-gmp=6.1.0=2
  - m2w64-gsl=2.1=2
  - m2w64-libiconv=1.14=6
  - m2w64-libjpeg-turbo=1.4.2=3
  - m2w64-libogg=1.3.2=3
  - m2w64-libpng=1.6.21=2
  - m2w64-libsndfile=1.0.26=2
  - m2w64-libtiff=4.0.6=2
  - m2w64-libvorbis=1.3.5=2
  - m2w64-libwinpthread-git=5.0.0.4634.697f757=2
  - m2w64-libxml2=2.9.3=4
  - m2w64-mpfr=3.1.4=4
  - m2w64-nlopt=2.4.2=3
  - m2w64-openblas=0.2.19=1
  - m2w64-pcre=8.38=2
  - m2w64-speex=1.2rc2=3
  - m2w64-speexdsp=1.2rc3=3
  - m2w64-tcl=8.6.5=3
  - m2w64-tk=8.6.5=3
  - m2w64-tktable=2.10=5
  - m2w64-wineditline=2.101=5
  - m2w64-xz=5.2.2=2
  - m2w64-zlib=1.2.8=10
  - markupsafe=1.1.1=py37he774522_0
  - mistune=0.8.4=py37he774522_0
  - mkl=2019.4=245
  - mkl-service=2.3.0=py37hb782905_0
  - mkl_fft=1.0.15=py37h14836fe_0
  - mkl_random=1.1.0=py37h675688f_0
  - msys2-conda-epoch=20160418=1
  - nbconvert=5.6.1=py37_0
  - nbformat=5.0.4=py_0
  - notebook=6.0.3=py37_0
  - numpy=1.17.4=py37h4320e6b_0
  - numpy-base=1.17.4=py37hc3f5095_0
  - openssl=1.1.1e=he774522_0
  - pandas=0.25.3=py37ha925a31_0
  - pandoc=2.2.3.2=0
  - pandocfilters=1.4.2=py37_1
  - parso=0.6.2=py_0
  - pickleshare=0.7.5=py37_0
  - pip=20.0.2=py37_1
  - plotly=4.4.1=py_0
  - plotly-orca=1.2.1=1
  - prometheus_client=0.7.1=py_0
  - prompt-toolkit=3.0.4=py_0
  - prompt_toolkit=3.0.4=0
  - psutil=5.6.7=py37he774522_0
  - pycparser=2.20=py_0
  - pygments=2.6.1=py_0
  - pyopenssl=19.1.0=py37_0
  - pyqt=5.9.2=py37h6538335_2
  - pyrsistent=0.16.0=py37he774522_0
  - pysocks=1.7.1=py37_0
  - python=3.7.5=h8c8aaf0_0
  - python-dateutil=2.8.1=py_0
  - pytz=2019.3=py_0
  - pywin32=227=py37he774522_1
  - pywinpty=0.5.7=py37_0
  - pyyaml=5.3.1=py37he774522_0
  - pyzmq=18.1.1=py37ha925a31_0
  - qt=5.9.7=vc14h73c81de_0
  - qtconsole=4.7.2=py_0
  - qtpy=1.9.0=py_0
  - r-abind=1.4_5=r36h6115d3f_0
  - r-assertthat=0.2.1=r36h6115d3f_0
  - r-base=3.6.1=hf18239d_1
  - r-bh=1.69.0_1=r36h6115d3f_0
  - r-bit=1.1_14=r36h6115d3f_0
  - r-bit64=0.9_7=r36h6115d3f_0
  - r-blob=1.1.1=r36h6115d3f_0
  - r-boot=1.3_20=r36h6115d3f_0
  - r-car=3.0_2=r36h6115d3f_0
  - r-cardata=3.0_2=r36h6115d3f_0
  - r-cellranger=1.1.0=r36h6115d3f_0
  - r-cli=1.1.0=r36h6115d3f_0
  - r-clipr=0.6.0=r36h6115d3f_0
  - r-crayon=1.3.4=r36h6115d3f_0
  - r-curl=3.3=r36h6115d3f_0
  - r-data.table=1.12.2=r36h6115d3f_0
  - r-dbi=1.0.0=r36h6115d3f_0
  - r-dbplyr=1.4.0=r36h6115d3f_0
  - r-digest=0.6.18=r36h6115d3f_0
  - r-dplyr=0.8.0.1=r36h6115d3f_0
  - r-ellipsis=0.1.0=r36h6115d3f_0
  - r-fansi=0.4.0=r36h6115d3f_0
  - r-forcats=0.4.0=r36h6115d3f_0
  - r-foreign=0.8_71=r36h6115d3f_0
  - r-glue=1.3.1=r36h6115d3f_0
  - r-haven=2.1.0=r36h6115d3f_0
  - r-hms=0.4.2=r36h6115d3f_0
  - r-lattice=0.20_38=r36h6115d3f_0
  - r-lme4=1.1_21=r36h6115d3f_0
  - r-magrittr=1.5=r36h6115d3f_4
  - r-maptools=0.9_5=r36h6115d3f_0
  - r-mass=7.3_51.3=r36h6115d3f_0
  - r-matrix=1.2_17=r36h6115d3f_0
  - r-matrixmodels=0.4_1=r36h6115d3f_4
  - r-memoise=1.1.0=r36h6115d3f_0
  - r-mgcv=1.8_28=r36h6115d3f_0
  - r-minqa=1.2.4=r36h6115d3f_4
  - r-nlme=3.1_139=r36h6115d3f_0
  - r-nloptr=1.2.1=r36h6115d3f_0
  - r-nnet=7.3_12=r36h6115d3f_0
  - r-openxlsx=4.1.0=r36h6115d3f_0
  - r-pbkrtest=0.4_7=r36h6115d3f_0
  - r-pillar=1.3.1=r36h6115d3f_0
  - r-pkgconfig=2.0.2=r36h6115d3f_0
  - r-plogr=0.2.0=r36h6115d3f_0
  - r-prettyunits=1.0.2=r36h6115d3f_0
  - r-progress=1.2.0=r36h6115d3f_0
  - r-purrr=0.3.2=r36h6115d3f_0
  - r-quantreg=5.38=r36h6115d3f_0
  - r-r6=2.4.0=r36h6115d3f_0
  - r-rcpp=1.0.1=r36h6115d3f_0
  - r-rcppeigen=0.3.3.5.0=r36h6115d3f_0
  - r-readr=1.3.1=r36h6115d3f_0
  - r-readxl=1.3.1=r36h6115d3f_0
  - r-rematch=1.0.1=r36h6115d3f_0
  - r-rio=0.5.16=r36h6115d3f_0
  - r-rlang=0.3.4=r36h6115d3f_0
  - r-rsqlite=2.1.1=r36h6115d3f_0
  - r-sp=1.3_1=r36h6115d3f_0
  - r-sparsem=1.77=r36h6115d3f_0
  - r-tibble=2.1.1=r36h6115d3f_0
  - r-tidyselect=0.2.5=r36h6115d3f_0
  - r-utf8=1.1.4=r36h6115d3f_0
  - r-zip=2.0.1=r36h6115d3f_0
  - requests=2.22.0=py37_1
  - retrying=1.3.3=py37_2
  - rpy2=2.9.4=py37r36h39e3cac_0
  - scipy=1.3.2=py37h29ff71c_0
  - send2trash=1.5.0=py37_0
  - setuptools=46.1.3=py37_0
  - sip=4.19.8=py37h6538335_0
  - six=1.14.0=py37_0
  - sqlite=3.31.1=he774522_0
  - terminado=0.8.3=py37_0
  - testpath=0.4.4=py_0
  - tornado=6.0.4=py37he774522_1
  - traitlets=4.3.3=py37_0
  - tzlocal=2.0.0=py37_0
  - urllib3=1.25.8=py37_0
  - vc=14.1=h0510ff6_4
  - vs2015_runtime=14.16.27012=hf0eaf9b_1
  - wcwidth=0.1.9=py_0
  - webencodings=0.5.1=py37_1
  - wheel=0.34.2=py37_0
  - widgetsnbextension=3.5.1=py37_0
  - win_inet_pton=1.1.0=py37_0
  - wincertstore=0.2=py37_0
  - winpty=0.4.3=4
  - xmltodict=0.12.0=py_0
  - yaml=0.1.7=hc54c509_2
  - zeromq=4.3.1=h33f27b4_3
  - zipp=2.2.0=py_0
  - zlib=1.2.11=h62dcd97_3
  - pip:
    - toolz==0.10.0
prefix: C:\Users\smanz\Anaconda3\envs\Env5_metcages

By Sumeed Yoyo Manzoor

smanzoor@uchicago.edu

PCA of RMR data from metabolic cages

Sumeed ManzoorChang Lab at the University of Chicago

May 15, 2020, updated May 23, 2020

dataset

Overview

SPF

GF

libraries

create data

import data

functions

data prep

datatables

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Hide

VO2

RQ

BodyMass

Food

Water

PedMeters

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VO2

RQ

BodyMass

Food

Water

PedMeters

basic graphs

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VO2

RQ

BodyMass

Food

Water

PedMeters

Dark

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VO2

RQ

BodyMass

Food

Water

PedMeters

PCA

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Local Fisher Discriminant Analysis (LDFA)

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session info

Python environment

Sumeed Manzoor
Chang Lab at the University of Chicago